The Change: Strikeouts Minus Walks, But With Popups

We know that strikeout minus walk percentage is the best in-season predictor, or at least we knew that the last time someone checked. We know that pop-ups are automatic outs, and that they have the same season-to-season correlation (.49) as strikes thrown, or at least we know that if you define pop-ups as infield flies per ball in play (PU/BIP) instead of just IFFB (which is PU/FB). And that means we know that these three metrics are three of the strongest by year-to-year correlation, at least among the metrics that the pitcher has the most control over.

Since we ‘know’ these things about as well as you can know things in baseball, it seems about right to combine them into a simple metric. Strikeouts plus pop-ups (the good things) minus walks (the bad things). It’s a quick and easy way to rank pitchers based on what they actually did last year, and it’s how I’ll sort my rankings the very first time I start working on them.

Particularly because I *really* like what it does at the top. Because pop-ups range between 1% (other than Brett Anderson at 0.3%) and 6% (other than Chris Young at 8.6%) generally, they’ll have less of an effect on this new stat than strikeout rate. For fantasy purposes, that’s good — strikeouts are a category. I also used 100 innings as the cutoff last year, even though 120 or so might be when pop-ups stabilize in a given season. I wanted to get a few fringe cases on the list in case they were interesting.

Here are the top 50 pitchers in KPU-BB%. The full spreadsheet is linked here.

2015’s Best in Strikeouts Plus Popups Minus Walks
Name IP GB% HR/FB ERA Soft% BABIP K% BB% PU% KPU-BB
Max Scherzer 228.2 36.0% 10.5% 2.79 20.9% 0.268 30.7% 3.8% 5.9% 32.8%
Clayton Kershaw 232.2 50.0% 10.1% 2.13 19.9% 0.281 33.8% 4.7% 2.7% 31.8%
Chris Sale 208.2 42.6% 12.5% 3.41 21.0% 0.323 32.1% 4.9% 3.5% 30.7%
Stephen Strasburg 127.1 42.2% 12.4% 3.46 21.8% 0.311 29.6% 5.0% 4.9% 29.5%
Madison Bumgarner 218.1 41.7% 10.2% 2.93 19.2% 0.282 26.9% 4.5% 4.3% 26.7%
Noah Syndergaard 150.0 46.5% 14.3% 3.24 19.9% 0.279 27.5% 5.1% 3.5% 25.9%
Carlos Carrasco 183.2 51.2% 13.2% 3.63 17.6% 0.304 29.6% 5.9% 2.0% 25.7%
Corey Kluber 222.0 42.4% 10.7% 3.49 17.8% 0.297 27.7% 5.1% 2.8% 25.4%
Jacob deGrom 191.0 44.4% 9.5% 2.54 19.6% 0.271 27.3% 5.1% 3.1% 25.3%
Chris Archer 212.0 46.1% 10.4% 3.23 18.1% 0.295 29.0% 7.6% 2.6% 24.0%
David Price 220.1 40.4% 7.8% 2.45 17.0% 0.290 25.3% 5.3% 4.0% 24.0%
Jake Arrieta 229.0 56.2% 7.8% 1.77 22.9% 0.246 27.1% 5.5% 2.1% 23.7%
Matt Harvey 189.1 46.0% 9.8% 2.71 17.2% 0.272 24.9% 4.9% 3.5% 23.5%
Travis Wood 100.2 34.5% 10.0% 3.84 18.0% 0.300 28.2% 9.3% 4.3% 23.2%
Michael Pineda 160.2 48.2% 14.7% 4.37 17.8% 0.332 23.4% 3.1% 2.7% 23.0%
Zack Greinke 222.2 48.0% 7.3% 1.66 21.7% 0.229 23.7% 4.7% 3.1% 22.1%
Jon Lester 205.0 48.9% 9.9% 3.34 21.4% 0.303 25.0% 5.7% 2.5% 21.8%
Masahiro Tanaka 154.0 47.0% 16.9% 3.51 19.3% 0.242 22.8% 4.4% 3.2% 21.6%
Justin Verlander 133.1 34.6% 7.5% 3.38 18.9% 0.267 21.1% 6.0% 6.3% 21.4%
Trevor May 114.2 39.0% 8.0% 4.00 15.3% 0.340 22.4% 5.3% 4.0% 21.1%
Cole Hamels 212.1 47.7% 12.0% 3.65 21.6% 0.294 24.4% 7.1% 3.8% 21.1%
Danny Salazar 185.0 43.9% 12.4% 3.45 17.1% 0.278 25.8% 7.0% 2.2% 21.0%
Gerrit Cole 208.0 48.0% 6.5% 2.60 20.0% 0.304 24.3% 5.3% 1.8% 20.8%
Mike Fiers 180.1 37.6% 11.3% 3.69 19.8% 0.283 23.7% 8.4% 5.3% 20.6%
Dallas Keuchel 232.0 61.7% 13.6% 2.48 25.2% 0.269 23.7% 5.6% 2.4% 20.5%
Taijuan Walker 169.2 38.6% 13.0% 4.56 16.8% 0.291 22.2% 5.7% 3.9% 20.4%
Clay Buchholz 113.1 48.3% 5.9% 3.26 19.2% 0.329 22.8% 4.9% 2.4% 20.3%
Kevin Gausman 112.1 44.7% 13.4% 4.25 23.5% 0.288 21.9% 6.2% 4.5% 20.2%
Ian Kennedy 168.1 38.5% 17.2% 4.28 14.6% 0.301 24.4% 7.3% 3.0% 20.1%
Jason Hammel 170.2 38.3% 12.8% 3.74 18.7% 0.288 24.2% 5.6% 1.5% 20.1%
Francisco Liriano 186.2 51.2% 11.7% 3.38 25.4% 0.293 26.5% 9.1% 2.5% 19.9%
Wei-Yin Chen 191.1 40.5% 12.3% 3.34 21.9% 0.290 19.3% 5.2% 5.5% 19.6%
Hisashi Iwakuma 129.2 50.4% 15.3% 3.54 16.2% 0.271 21.5% 4.1% 2.1% 19.5%
Lance McCullers 125.2 46.5% 9.3% 3.22 21.0% 0.288 24.8% 8.3% 3.0% 19.5%
Jordan Zimmermann 201.2 42.0% 10.9% 3.66 20.2% 0.302 19.7% 4.7% 4.5% 19.5%
Johnny Cueto 212.0 42.5% 9.5% 3.44 19.8% 0.281 20.3% 5.3% 4.3% 19.3%
James Shields 202.1 44.9% 17.6% 3.91 17.2% 0.299 25.1% 9.4% 3.5% 19.2%
Kyle Hendricks 180.0 51.3% 12.4% 3.95 18.7% 0.296 22.6% 5.8% 2.4% 19.2%
Jake Odorizzi 169.1 37.3% 9.0% 3.35 19.4% 0.271 21.4% 6.6% 4.3% 19.1%
J.A. Happ 172.0 41.6% 9.2% 3.61 17.9% 0.312 21.1% 6.3% 4.1% 18.9%
Jesse Chavez 157.0 43.1% 11.0% 4.18 18.9% 0.312 20.2% 7.1% 5.4% 18.5%
Anibal Sanchez 157.0 40.0% 16.0% 4.99 18.6% 0.278 20.9% 7.4% 5.0% 18.5%
Matt Shoemaker 135.1 39.2% 14.0% 4.46 18.1% 0.285 20.4% 6.2% 3.9% 18.1%
Felix Hernandez 201.2 56.2% 15.3% 3.53 18.3% 0.288 23.1% 7.0% 2.0% 18.1%
Trevor Bauer 176.0 39.2% 11.7% 4.55 21.4% 0.276 22.9% 10.6% 5.6% 17.9%
Nate Karns 147.0 41.9% 12.8% 3.67 19.5% 0.285 23.4% 9.0% 3.4% 17.8%
Carlos Martinez 179.2 54.5% 10.6% 3.01 21.2% 0.318 24.4% 8.3% 1.7% 17.8%
Dan Haren 187.1 30.6% 11.0% 3.60 18.3% 0.256 17.2% 5.0% 5.4% 17.6%
Collin McHugh 203.2 45.4% 8.9% 3.89 21.4% 0.310 19.9% 6.2% 3.9% 17.6%
Hector Santiago 180.2 29.9% 10.2% 3.59 15.2% 0.252 20.9% 9.2% 5.9% 17.6%
PU% = IFFB*FB%

The top 20% in KPU-BB had a .288 batting average on balls in play and had a collective 19.6% soft-hit rate, which is above the 18.3% league average for starting pitchers — so there is some evidence that there is a benefit beyond what’s being caught already in FIP. Pop-ups have been added to FIP, but we’ve removed home runs here because there may actually be a way to get more pop-ups and give up fewer home runs. The group did average .9 home runs per nine, which is less than the average 1.06 per nine from starters, and also did so with a 10.8% home run per fly ball rate, which is less than the 11.6% league average was last year.

And at the top, it’s a who’s who of the best pitchers in baseball. With a loose sorting that I really like, other than perhaps Noah Syndergaard’s high placement — which really should only be lower because the lack of a long statistical track record in the big leagues.

You can quibble with Travis Wood’s inclusion, but he’s really just a reliever masquerading as a starer to get into this list. Once you hit that speed bump, you have to get all the way down to Justin Verlander and Trevor May before you scratch your head again. And May is probably another case of being pumped up by relieving.

But Verlander’s high fastball usage last year was a major driver of his improvement, and so was the best pop-up rate of his career. He’s always been better than average (3.5%), but last year he jumped from his career average (4.7%) to a new high (6.3%). He was third in baseball last year in pop-up rate! Like Verlander, Mike Fiers has always had a lot of popups (5.0% career) based on throwing a riding fastball high in the zone. You can believe he’ll do it again, since he’s been better than average every year in his career.

But Kevin Gausman is new to this sort of thing. He was terrible at getting cans of corn in a small sample in 2013 (.8%), league average in 2014 (3.5%), and a league leader in 2015 (4.5%, or 25th of 141). Gausman’s threw his four-seam, on average, two and a quarter inches higher in 2015 than he did in 2014. His pop-up rate on the pitch itself doubled. We’ve spent a lot of time waiting on Gausman, and maybe that curve really is the breaking ball he needed — a double-digit swinging strike rate, a decent strikeout rate, a good walk rate, these things were all there last year, already. Just a little more work on the home run rate, and Gausman will arrive like we thought he might.

Of course, you can see that the skill may be well defined, but it doesn’t have a large spread. A standard deviation is 1.4% in pop-up rate, and 4.5% in strikeout rate. But those are automatic outs, and so it’s still interesting to highlight the leaders in this way.

Wei-Yin Chen has mastered this for years. Jordan Zimmermann is a sneaky pop-up guy, and adds excellent command. Johnny Cueto! Right there, you have three of the guys that have made people scratch their heads the most. Maybe Matt Cain has shown us the fragility of this approach when it comes to longevity, or maybe Matt Cain just got hurt. These three are all very good at getting pop-ups.

If we’re looking for the next generation of of that trio, maybe we can find it in Trevor Bauer and Jake Odorizzi. You can find flaws in some of their pitches — Odorizzi hasn’t yet found the breaking ball he needs to be excellent — but they do understand how to use their fastball for automatic outs that aren’t strikeouts. Both of them throw their four-seam fastball more often in the upper half of the zone than the bottom.

Sometimes, the lack of the pop-up is a secret stealer of value. Hisashi Iwakuma has never once managed a league-average pop-up rate. His strikeout minus walk rate without this addition would rate him higher. But he prefers to get ground-balls, and since some of the decision to get pop-ups includes a willingness to pitch higher in the zone, where fewer groundballs are born, then it’s an either/or situation.

The bottom of the list has plenty of ground-ball wizards with no pop-ups and few swinging strike pitches. Last place, Williams Perez, is basically the one-man encapsulation of that concept, though Tim Hudson, Mike Pelfrey, Brett Anderson, and Doug Fister found themselves in the bottom 15%, too. You get the type.

Check out the full list. as you get into the 50s and 60s, you see how a few popups could make a questionable fringe pitcher a better mixed-league option than might first appear. Erasmo Ramirez, Eduardo Rodriguez, Phil Hughes, and Ervin Santana all sneak into the late 60s and early 70s on the strength of their pop-up rates. And for the most part, that skill should return to give them sneaky value next season.





With a phone full of pictures of pitchers' fingers, strange beers, and his two toddler sons, Eno Sarris can be found at the ballpark or a brewery most days. Read him here, writing about the A's or Giants at The Athletic, or about beer at October. Follow him on Twitter @enosarris if you can handle the sandwiches and inanity.

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Dustin
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Dustin

I might be missing it, but why not use TBF as the denominator for the PU% as well? K% and BB% are K/TBF and BB/TBF, why not use IFFB/TBF?